Keyword landscape analysis

set.seed(2)

required_packages <- c("tidyverse", "magrittr", "DBI", "bigrquery", "arrow","glue", "vroom","janitor", "gt", "ggwordcloud", "readxl", "ggthemes", "hrbrthemes", "extrafont", "plotly", "scales", "stringr", "gganimate", "here", "tidytext", "sentimentr", "scales", "DT", "here", "sm", "mblm", "glue", "fs", "knitr", "rmdformats", "janitor", "urltools", "colorspace", "pdftools", "showtext", "pander")
for(i in required_packages) { 
  if(!require(i, character.only = T)) {
    #  if package is not existing, install then load the package
    install.packages(i, dependencies = T)
  require(i, character.only = T)
  }
}

panderOptions('table.alignment.default', "left")

## quality of png's
dpi <- 750

## theme updates; please adjust to client´s website
#theme_set(ggthemes::theme_clean(base_size = 15))
theme_set(ggthemes::theme_clean(base_size = 15, base_family = "Montserrat"))


theme_update(plot.margin = margin(30, 30, 30, 30),
             plot.background = element_rect(color = "white",
                                            fill = "white"),
             plot.title = element_text(size = 20,
                                       face = "bold",
                                       lineheight = 1.05,
                                       hjust = .5,
                                       margin = margin(10, 0, 25, 0)),
             plot.title.position = "plot",
             plot.caption = element_text(color = "grey40",
                                         size = 9,
                                         margin = margin(20, 0, -20, 0)),
             plot.caption.position = "plot",
             axis.line.x = element_line(color = "black",
                                        size = .8),
             axis.line.y = element_line(color = "black",
                                        size = .8),
             axis.title.x = element_text(size = 16,
                                         face = "bold",
                                         margin = margin(t = 20)),
             axis.title.y = element_text(size = 16,
                                         face = "bold",
                                         margin = margin(r = 20)),
             axis.text = element_text(size = 11,
                                      color = "black",
                                      face = "bold"),
             axis.text.x = element_text(margin = margin(t = 10)),
             axis.text.y = element_text(margin = margin(r = 10)),
             axis.ticks = element_blank(),
             panel.grid.major.x = element_line(size = .6,
                                               color = "#eaeaea",
                                               linetype = "solid"),
             panel.grid.major.y = element_line(size = .6,
                                               color = "#eaeaea",
                                               linetype = "solid"),
             panel.grid.minor.x = element_line(size = .6,
                                               color = "#eaeaea",
                                               linetype = "solid"),
             panel.grid.minor.y = element_blank(),
             panel.spacing.x = unit(4, "lines"),
             panel.spacing.y = unit(2, "lines"),
             legend.position = "top",
             legend.title = element_text(family = "Montserrat",
                                         color = "black",
                                         size = 14,
                                         margin = margin(5, 0, 5, 0)),
             legend.text = element_text(family = "Montserrat",
                                        color = "black",
                                        size = 11,
                                        margin = margin(4.5, 4.5, 4.5, 4.5)),
             legend.background = element_rect(fill = NA,
                                              color = NA),
             legend.key = element_rect(color = NA, fill = NA),
             #legend.key.width = unit(5, "lines"),
             #legend.spacing.x = unit(.05, "pt"),
             #legend.spacing.y = unit(.55, "pt"),
             #legend.margin = margin(0, 0, 10, 0),
             strip.text = element_text(face = "bold",
                                       margin = margin(b = 10)))

## theme settings for flipped plots
theme_flip <-
  theme(panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_line(size = .6,
                                          color = "#eaeaea"))

## theme settings for maps
theme_map <- 
  theme_void(base_family = "Montserrat") +
  theme(legend.direction = "horizontal",
        legend.box = "horizontal",
        legend.margin = margin(10, 10, 10, 10),
        legend.title = element_text(size = 17, 
                                    face = "bold"),
        legend.text = element_text(color = "grey33",
                                   size = 12),
        plot.margin = margin(15, 5, 15, 5),
        plot.title = element_text(face = "bold",
                                  size = 20,
                                  hjust = .5,
                                  margin = margin(30, 0, 10, 0)),
        plot.subtitle = element_text(face = "bold",
                                     color = "grey33",
                                     size = 17,
                                     hjust = .5,
                                     margin = margin(10, 0, -30, 0)),
        plot.caption = element_text(size = 14,
                                    color = "grey33",
                                    hjust = .97,
                                    margin = margin(-30, 0, 0, 0)))

## numeric format for labels
num_format <- scales::format_format(big.mark = ",", small.mark = ",", scientific = F)

## main color backlinko
bl_col <- "#00d188"
bl_dark <- darken(bl_col, .3, space = "HLS")

## colors + labels for interval stripes
int_cols <- c("#bce2d5", "#79d8b6", bl_col, "#009f66", "#006c45", "#003925")
int_perc <- c("100%", "95%", "75%", "50%", "25%", "5%")

## colors for degrees (Bachelors, Massters, Doctorate in reverse order)
cols_degree <- c("#e64500", "#FFCC00", darken(bl_col, .1))

## gradient colors for position
colfunc <- colorRampPalette(c(bl_col, "#bce2d5"))
pos_cols <- colfunc(10)

display_number <- function(n, title){
  tibble(!!sym(title) := format(n, big.mark = ",")) %>% pander()
}

con <- dbConnect(
    bigrquery::bigquery(),
    project = "dataforseo-bigquery",
    billing = "dataforseo-bigquery"
)

sql <- glue("SELECT * FROM `dataforseo-bigquery.dataforseo_data.keyword_data` 
          WHERE location = 2840 
          ORDER BY keyword_info_search_volume DESC
          LIMIT 50000")
tb <- bq_project_query("dataforseo-bigquery", sql)
top <- bq_table_download(tb, max_results = 1000000)

Basic stats

SELECT COUNT(keyword_info_search_volume) as `total_count`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE `location` = 2840

total_count <- sql$total_count
display_number(total_count, "Total number of different searches")

Total number of different searches
580,082,623

SELECT SUM(COALESCE(keyword_info_search_volume / 10000, 0)) AS total_volume
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE location = 2840

# Calculated in a roundabout way to avoid integer overflow
total_volume <- sql$total_volume * 10000
tibble("Total number of searches" = glue("~{round(total_volume / 1000000)} million")) %>% 
  pander()

Total number of searches
~302567 million

This table shows the top 10 searches. They are almost all spelling errors. As in, they are not really searched, but rather people attempting to go to Youtube or Facebook, but typing it wrong. Oddly they are all attributed as having a search volume of exactly 185 million.

top %>% 
  select(keyword, location, spell, spell_type, keyword_info_search_volume) %>% 
  head(10) %>% 
  gt() %>% 
  tab_options(table.align = "left") %>% 
  tab_header("Top searches")

Top searches
keyword	location	spell	spell_type	keyword_info_search_volume
face bak	2840	facebook	did_you_mean	185000000
yput	2840	youtube	did_you_mean	185000000
xyoutube	2840	youtube	showing_results_for	185000000
utub	2840	youtube	did_you_mean	185000000
yioutub	2840	youtube	showing_results_for	185000000
fa9ebook	2840	facebook	showing_results_for	185000000
ioyoutube	2840	youtube	showing_results_for	185000000
kn youtube	2840	on	did_you_mean	185000000
the only youtube	2840			185000000
youtbe ==	2840	youtube	did_you_mean	185000000

SELECT COUNT(*) as `missing_count`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE `location` = 2840
AND keyword_info_search_volume IS NULL

missing_count <- sql$missing_count
tibble("Missing search volume" = scales::percent(missing_count / total_count, accuracy = 0.001)) %>% pander()

Missing search volume
0.514%

The missing have some searches that are likely high volume. Thus they are truly missing, and not just 0s.

SELECT keyword, keyword_info_search_volume
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE `location` = 2840
AND keyword_info_search_volume IS NULL
ORDER BY RAND()
LIMIT 10

sql %>% gt() %>% tab_options(table.align = "left") %>% 
  tab_header("Keywords with missing search volume")

Keywords with missing search volume
keyword	keyword_info_search_volume
Gain detergent reviews	NA
What is Jacksepticeye's real name	NA
The Odyssey Book 5 Summary	NA
Best BBQ in Memphis Reddit	NA
Fast fashion unsustainable	NA
Herbs to lower cholesterol and blood pressure	NA
SFC VISAプラチナ	NA
Decorative Wire Garland	NA
Transmission linkage	NA
Minecraft mini games Realms	NA

SELECT COUNT(*) as `zero_count`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE `location` = 2840
AND keyword_info_search_volume = 0

zero_count <- sql$zero_count
tibble("Searches with search volume 0" = scales::percent(zero_count / total_count, accuracy = 0.001)) %>% pander()

Searches with search volume 0
47.243%

display_number(total_volume / total_count, "Mean search volume")

Mean search volume
521.5929

SELECT approx_quantiles(keyword_info_search_volume, 2)[offset(1)] AS `median`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE location = 2840

display_number(sql$median, "Median search volume")

Median search volume
10

SELECT AVG(`keyword_info_cpc`) AS `mean_cpc`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE `location` = 2840

display_number(sql$mean_cpc, "Mean CPC")

Mean CPC
0.3202192

SELECT approx_quantiles(keyword_info_cpc, 2)[offset(1)] AS `median_cpc`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE location = 2840

display_number(sql$median_cpc, "Median CPC")

Median CPC
0

Spell types

SELECT *
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE location = 2840
AND spell_type <> ""
AND keyword_info_search_volume < 10000
LIMIT 1000

SELECT spell_type, SUM(keyword_info_search_volume) / 10000 AS `volume`
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE location = 2840
GROUP BY spell_type

spell_types <- sql %>% mutate(spell_type = ifelse(spell_type == "", "no_spell_type", spell_type)) %>% 
  mutate(volume = volume * 10000) 


spell_types %>% ggplot(aes(x = reorder(spell_type, volume), y = volume)) +
  geom_bar(stat = "identity", width = 0.8, fill = "turquoise4", color = "black") +
  labs(x = "", title = "Spell types")

About half of search volume has a spell type. This is especially driven by misspellings of common domains.

spell_types %>% filter(spell_type != "no_spell_type") %$% 
  sum(volume) / total_volume

## [1] 0.4788949

top %>% group_by(spell) %>% 
  summarise(volume = sum(keyword_info_search_volume)) %>% 
  arrange(desc(volume)) %>% 
  filter(spell != "") %>% 
  head(15) %>% 
  gt() %>% 
  tab_options(table.align = "left") %>% 
  tab_header("Top 15 intended searches that are misspelled")

Top 15 intended searches that are misspelled
spell	volume
youtube	46879296000
facebook	11555936000
amazon	10041630000
google	8313882000
weather	2955470000
translate	2098786000
com	1994509000
instagram	1783527000
walmart	1731168000
ebay	1566900000
yahoo	1422318000
youtubecom	925000000
netflix	920823000
news	910156000
you	789786000

Questions

question_words <- c("what", "which", "where", "who", "why", "how")
questions <- tribble(~question, ~volume)
for (word in question_words){
  sql <- glue("SELECT sum(keyword_info_search_volume) FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
            WHERE location = 2840 
            AND keyword like '{word} %' ")
  tb <- bq_project_query("dataforseo-bigquery", sql)
  df <- bq_table_download(tb) 
  questions %<>% add_row(question = word, volume = df$f0_)
}

questions %>% mutate(prop = volume / total_volume) %>% 
  ggplot(aes(x = reorder(question, prop), y = prop)) +
  geom_bar(stat = "identity", fill = "turquoise4", color = "black") +
  scale_y_continuous(labels = scales::percent, limits = c(0, 0.005), expand = c(0,0)) +
  labs(title = "Questions in searches", x = "", y = "")

Total percentage of questions if spell types are removed:

SELECT SUM(COALESCE(keyword_info_search_volume / 10000, 0)) AS total_volume
FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
WHERE location = 2840
AND spell_type = ""

total_volume_nospell <- sql$total_volume * 10000

tibble("total percentage of questions if psell types are removed" = 
         scales::percent(questions %$% sum(volume) / total_volume_nospell, accuracy = 0.001)) %>% 
  pander()

total percentage of questions if psell types are removed
1.547%

Stopwords

stopwords_list <- tibble(stopword = stopwords::stopwords(language = "en")) %>% 
  mutate(stopword = str_remove(stopword, "'")) %>% 
  filter(!(stopword %in% c("shed", "wed", "ill", "hell", "shell")))

get_stopwords_counts <- function(){
stopwords <- tribble(~stopword, ~volume)
  for (word in stopword_list$stopword){
    print(word)
    sql <- glue(
      "SELECT SUM(COALESCE(keyword_info_search_volume / 1000, 0)) AS `total_volume` 
       FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
       WHERE location = 2840 
       AND keyword like '% {word} %' OR keyword like '{word} %' OR keyword like '% {word}'")
    tb <- bq_project_query("dataforseo-bigquery", sql)
  
    df <- bq_table_download(tb) 
    stopwords %<>% add_row(stopword = word, volume = df$total_volume * 1000)
  }
  
  write_csv(stopwords, "../proc_data/stopwords.csv")
}
stopwords <- read_csv("../proc_data/stopwords.csv")

stopwords %>% mutate(prop = volume / total_volume) %>%
  filter(prop > 0.001) %>% 
  ggplot(aes(x = reorder(stopword, prop), y = prop)) +
  geom_bar(stat = "identity", color = "black", fill = "turquoise4", width = 0.7) +
  scale_y_continuous(labels = scales::percent, expand = c(0,0), limits = c(0, 0.05)) +
  labs(x = "", y = "", title = "Proportion of searches with specific stopwords") +
  coord_flip()

set.seed(1)
stopwords %>% 
  mutate(stopword = ifelse(stopword == "i", "I", stopword)) %>% 
  arrange(desc(volume)) %>% 
  head(25) %>% 
  ggplot(aes(label = stopword, size = volume, color = factor(sample.int(10, 25, replace = TRUE)))) +
  geom_text_wordcloud(perc_step = 0.3) +
  scale_size_area(max_size = 30) +
  theme_minimal()

Search tails

volume_top <- top %>%  
  add_rownames() %>% 
  mutate(rowname = as.numeric(rowname)) %>% 
  select(rowname, volume = keyword_info_search_volume)

ylab <- c(50, 100, 150, 200)

volume_top %>% 
  mutate(cat = case_when(
    rowname < 500 ~ "Top 500",
    rowname < 1000 ~ "Top 1000",
    rowname < 2000 ~ "Top 2000",
    rowname < 5801 ~ "Top 0.001%",
    T ~ "Remaining 99.99%"
  )) %>% 
  mutate(cat = factor(cat, levels = c("Top 500", "Top 1000", "Top 2000", "Top 0.001%", "Remaining 99.99%"))) %>% 
  head(10500) %>% 
  ggplot(aes(x = rowname, y = volume, fill = cat)) +
  geom_area(alpha = 0.8) +
  scale_y_continuous(
    labels = glue("{ylab} M"),
    breaks = 10^6 * ylab,
    limits = c(0, 200* 10^6), 
    expand = c(0,0)
    ) +
  labs(x = "", title = "Long tail") + 
  geom_segment(aes(x = 8000, y = 35*10^6, xend = 10500, yend = 35*10^6),
               arrow = arrow(length = unit(0.5, "cm"))) +
  scale_x_continuous(expand = c(0,0))

get_count_range <-  function(lower, higher)
{
  sql <- glue(
        "SELECT COUNT(*) AS `count` 
         FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
         WHERE location = 2840 
         AND keyword_info_search_volume >= {lower} 
         AND keyword_info_search_volume <= {higher}")
      tb <- bq_project_query("dataforseo-bigquery", sql)
      bq_table_download(tb)$count
}

df <- tribble(
  ~cat, ~count,
  "0 - 10", get_count_range(0, 10),
  "11- 100", get_count_range(11, 100),
  "101 - 1000", get_count_range(101, 1000),
  "1001 - 10000", get_count_range(1001, 10000),
  "10001 - 100000", get_count_range(10001, "100000"),
  "100001+", get_count_range("100001", "100000000000")) 


df %>% 
  mutate(count = count / sum(count)) %>% 
  ggplot(aes(x = reorder(cat, desc(count)), y = count)) +
  geom_bar(stat = "identity", fill = "turquoise4", color = "black") +
  theme_light() +
  labs(x = "", y = "", title = "Long tail") +
  scale_y_continuous(labels = scales::percent, limits = c(0, 1), expand = c(0,0))

write_length_volume <- function()
{
  get_length_volume <-  function(l)
  {
    sql <- glue(
          "SELECT sum(keyword_info_search_volume) / 10000 as `volume`, count(keyword_info_search_volume) as `count`
           FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
           WHERE location = 2840
           AND keyword_info_search_volume IS NOT NULL
           AND LENGTH(keyword) = {l}")
        tb <- bq_project_query("dataforseo-bigquery", sql)
        bq_table_download(tb) %>% mutate(length = l)
  }
  
  df <- map_df(1:50, get_length_volume)  
  write_csv(df, "../proc_data/keyword_length_volume.csv")
}

df <- read_csv("../proc_data/keyword_length_volume.csv")

df %>% mutate(
  volume = 10000 * volume,
  ratio = volume / count
) %>% 
  ggplot(aes(x = length, y = ratio)) +
  geom_line(color = "turquoise4", size = 1) + geom_point(color = "turquoise4") +
  theme_light() +
  labs(x = "", y = "", title = "Average search volume by keyword length")

Keyword_info categories

pservices <- read_csv("../raw_data/productsservices.csv") %>% 
  clean_names() %>% rename(c1 = criterion_id) %>% select(-category) %>% 
  separate(c1, sep =",\"", into = c("id", "category")) %>% 
  mutate(category = substr(category, 2, nchar(category) -1)) %>% 
  separate(category, sep = "/", into = c("cat1", "cat2", "cat3", "cat4", "cat5", "cat6", "cat7", "cat8"))

toplevel <- pservices %>% filter(is.na(cat2))

write_categories <- function()
{
  get_category_volume <-  function(id){
    sql <- glue(
      "SELECT SUM(keyword_info_search_volume) / 10000 AS `search_volume`, AVG(keyword_info_cpc) AS `cpc`, COUNT(*) AS `count`
       FROM `dataforseo-bigquery.dataforseo_data.keyword_data`
       WHERE location = 2840 
       AND keyword_info_categories like '%{id}%' ")
    tb <- bq_project_query("dataforseo-bigquery", sql)
    bq_table_download(tb) %>% mutate(id = id)
  }

  df <- map_df(toplevel$id, get_category_volume)  
  
  df %>% mutate(search_volume = search_volume * 10000,
                mean_volume = search_volume / count
                ) %>% 
    left_join(toplevel %>% select(id, cat1), by = "id") %>% 
    write_csv("../proc_data/categories_averages.csv")
}

df <- read_csv("../proc_data/categories_averages.csv")

df %>% 
  ggplot(aes(x = fct_rev(cat1), y = mean_volume)) +
  geom_bar(stat = "identity", fill = "turquoise4", color = "black", width = 0.8) +
  theme_light() +
  coord_flip() +
  scale_y_continuous(limits = c(0, 9000), expand = c(0,0)) +
  labs(x = "", y = "", title = "Search volume mean by category")

df %>% 
  ggplot(aes(x = fct_rev(cat1), y = cpc)) +
  geom_bar(stat = "identity", fill = "turquoise4", color = "black", width = 0.8) +
  theme_light() +
  coord_flip() +
  scale_y_continuous(limits = c(0, 2), expand = c(0,0)) +
  labs(x = "", y = "", title = "CPC mean by category")